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Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems.

This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.

In High Efficiency Video Coding (HEVC), multiple-QP (quantization parameter) optimization can adapt to a local video content. However, the multiple-QP implementation in the HEVC reference software (HM 16.6) achieves the best QP value for each coding block with a large amount of computational complexity.

Conventional video saliency detection methods frequently follow the common bottom-up thread to estimate video saliency within the short-term fashion. As a result, such methods can not avoid the obstinate accumulation of errors when the collected low-level clues are constantly ill-detected. Also, being noticed that a portion of video frames, which are not nearby the current video frame over the time axis, may potentially benefit the saliency detection in the current video frame.

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. 

Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear. This paper proposes an unmixing model, named illumination invariant spectral unmixing (IISU).

Modern System-on-Chip (SoC) designs integrate a number of third party IPs (3PIPs) that coordinate and communicate through a Network-on-Chip (NoC) fabric to realize system functionality. An important class of SoC security attack involves a rogue IP tampering with the inter-IP communication.